Random perturbation models and performance characterization in computer vision

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Abstract
It is shown how random perturbation models can be set up for a vision algorithm sequence involving edge finding, edge linking, and gap filling. By starting with an appropriate noise model for the input data, the authors derive random perturbation models for the output data at each stage of their example sequence. These random perturbation models are useful for performing model-based theoretical comparisons of the performance of vision algorithms. Parameters of these random perturbation models are related to measures of error such as the probability of misdetection of feature units, probability of false alarm, and the probability of incorrect grouping. Since the parameters of the perturbation model at the output of an algorithm are indicators of the performance of the algorithm, one could utilize these models to automate the selection of various free parameters (thresholds) of the algorithm.

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